mirror of
https://github.com/infiniteCable2/openpilot.git
synced 2026-09-04 15:23:42 +08:00
Jason Wen
acd46aa94b
* modeld: Retain pre-20hz drive model support * Method not available anymore on OP * some fixes * Revert "Long planner get accel: new function args (#34288)" * Revert "Fix low-speed allow_throttle behavior in long planner (#33894)" * Revert "long planner: allow throttle reflects usage (#33792)" * Revert "Gate acceleration on model gas press predictions (#33643)" * Reapply "Gate acceleration on model gas press predictions (#33643)" This reverts commit 76b08e37cb8eb94266ad9f6fed80db227e7c3428. * Reapply "long planner: allow throttle reflects usage (#33792)" This reverts commit c75244ca4e9c48084b0205b7c871e1a4e0f4e693. * Reapply "Fix low-speed allow_throttle behavior in long planner (#33894)" This reverts commit b2b7d21b7b685a2785d1beede3d223f0bb954807. * Reapply "Long planner get accel: new function args (#34288)" This reverts commit 74dca2fccf4da59cc8ac62ba9c0ad10ba3fc264b. * don't need * retain snpe * wrong * they're symlinks * remove * put back into VCS * add back * don't include built * Refactor model runner retrieval with caching support Added caching for active model runner type via `ModelRunnerTypeCache` to enhance performance and avoid redundant checks. Introduced a `force_check` flag to bypass the cache when necessary. Updated related code to handle cache clearing during onroad transitions. * Update model runner determination logic with caching fix Enhances `get_active_model_runner` to utilize caching more effectively by ensuring type consistency and updating cache only when necessary. Also updates `is_snpe_model` to pass the `started` state to the runner determination function, improving behavior for dynamic checks. * default to none * enable in next PR * more --------- Co-authored-by: DevTekVE <devtekve@gmail.com>
29 lines
1003 B
Python
Executable File
29 lines
1003 B
Python
Executable File
#!/usr/bin/env python3
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import sys
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import pathlib
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import onnx
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import codecs
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import pickle
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def get_name_and_shape(value_info:onnx.ValueInfoProto) -> tuple[str, tuple[int,...]]:
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shape = tuple([int(dim.dim_value) for dim in value_info.type.tensor_type.shape.dim])
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name = value_info.name
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return name, shape
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if __name__ == "__main__":
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model_path = pathlib.Path(sys.argv[1])
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model = onnx.load(str(model_path))
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i = [x.key for x in model.metadata_props].index('output_slices')
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output_slices = model.metadata_props[i].value
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metadata = {}
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metadata['output_slices'] = pickle.loads(codecs.decode(output_slices.encode(), "base64"))
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metadata['input_shapes'] = dict([get_name_and_shape(x) for x in model.graph.input])
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metadata['output_shapes'] = dict([get_name_and_shape(x) for x in model.graph.output])
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metadata_path = model_path.parent / (model_path.stem + '_metadata.pkl')
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with open(metadata_path, 'wb') as f:
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pickle.dump(metadata, f)
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print(f'saved metadata to {metadata_path}')
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